Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collections

ABSTRACT

A method of segmenting relative user preferences into fine-grain and coarse-grain collections is provided. The method includes providing a set of content items having associated descriptive terms. The method also includes receiving user search input and, in response thereto, presenting a subset of content items. The method includes receiving user selection actions and analyzing the selections to learn the user&#39;s preferred descriptive terms. The method includes expressing the learned preferred descriptive terms as a segmented probability distribution function having at least one fine grain segment and at least one coarse grain segment. In response to subsequent search input, the method calls for selecting and ordering a collection of content items by promoting the ranking of content items associated with the learned preferred descriptive terms of the user according to the differentiation provided by the segmented probability distribution function.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following applications, thecontents of which are incorporated by reference herein:

U.S. Provisional Application No. 60/779,547, entitled A Framework forLearning User Behavior With Stochastic Signatures, filed Mar. 6, 2006;

U.S. Provisional Application No. 60/784,027, entitled A System AndMethod For Service Solicitation Enhanced With Relevant Personal Contextto Elicit Targeted Response, filed Mar. 20, 2006;

U.S. Provisional Application No. 60/796,614, entitled A Learning ModelFor Multiple Dataspaces With Applications To Mobile Environment, filedMay 1, 2006; and

U.S. Provisional Application No. 60/834,966, entitled SeminormalizationOf Signatures For Reducing Truncation Errors And Stabilizing RelevancePromotion, filed Aug. 2, 2006.

This application is related to the following applications, filed on aneven date herewith:

U.S. patent application Ser. No. 11/682,693, entitled Methods andSystems For Selecting and Presenting Content Based On LearnedPeriodicity Of User Content Selection;

U.S. patent application Ser. No. 11/682,700, entitled Methods andSystems For Selecting and Presenting Content Based On DynamicallyIdentifying Microgenres Associated With The Content;

U.S. patent application Ser. No. 11/682,689, entitled Methods andSystems For Selecting and Presenting Content Based On Activity LevelSpikes Associated With The Content;

U.S. patent application Ser. No. 11/682,695, entitled Methods andSystems For Selecting and Presenting Content Based On User PreferenceInformation Extracted From An Aggregate Preference Signature;

U.S. patent application Ser. No. 11/682,533, entitled Methods andSystems For Selecting and Presenting Content Based On A Comparison OfPreference Signatures From Multiple Users;

U.S. patent application Ser. No. 11/682,588, entitled Methods andSystems For Selecting and Presenting Content On A First System Based OnUser Preferences Learned On A Second System; and

U.S. patent application Ser. No. 11/682,599, entitled Methods andSystems For Selecting and Presenting Content Based On Context SensitiveUser Preferences.

BACKGROUND OF THE INVENTION

1. Field of Invention

This invention generally relates to learning user preferences and, morespecifically, to using those preferences to personalize the user'sinteraction with various service providers and interactions with contentquery systems, e.g., to better find results to queries provided by theuser and to ordering the results for presentation to the user.

2. Description of Related Art

Personalization strategies to improve user experience can bechronologically classified into two categories: (1) collaborativefiltering and (2) content reordering. Each is summarized in turn.

Collaborative Filtering was used in the late 1990s to generaterecommendations for users. The term collaborative filtering refers toclustering users with similar interests and offering recommendations tousers in the cluster based on the habits of the other users. Twodistinct filtering techniques—user based and item based—are used infiltering.

In U.S. Patent App. Pub. No. U.S. 2005/0240580, Zamir et al. describe apersonalization approach for reordering search queries based on theuser's preferences. The application describes a technique for learningthe user's preferences and increasing the promotion level of a searchresult based on personalization. Zamir et al. create a user profile,which is a list of keywords and categories listing the user preferences.The profile is generated from multiple sources, such as (1) informationprovided by the user at the time the user registers a login, (2)information from queries that the user has submitted in the past, and(3) information from web pages the user has selected.

Some systems directed to reordering content in the context of televisionschedules define categories and sub-categories according to an acceptedstandard. User preferences are gathered using various models such as (1)user input, (2) stereotypical user models, and (3) unobtrusiveobservation of user viewing habits. In some implementations, thesemodels operate in parallel and collect the user preference information.

In other systems, a set of fixed attributes is defined and all mediacontent and all user preferences are classified using these attributes.A vector of attribute weights captures the media content and the userpreferences. The systems then determine the vector product between thecontent vector and the user preferences vector. The system suggestscontent to users where the values of the vector products exceed apredetermined threshold.

BRIEF SUMMARY OF THE INVENTION

The invention provided methods and systems for selecting and presentingcontent based segmenting relative user preferences into fine-grain andcoarse-grain collections.

Under an aspect of the invention, a user-interface method of selectingand presenting a collection of content items in which the presentationis ordered at least in part based on learned user preferences includesproviding a set of content items, wherein each content item has at leastone associated descriptive term to describe the content item. The methodalso includes receiving incremental input entered by the user forincrementally identifying desired content items and, in response to theincremental input entered by the user, presenting a subset of contentitems. The method also includes receiving selection actions of contentitems of the subset from the user and analyzing the descriptive termsassociated with the selected content items to learn the preferreddescriptive terms of the user. The method further includes expressingthe learned preferred descriptive terms as a segmented probabilitydistribution function having at least one fine grain segment and atleast one coarse grain segment. The fine grain segment has fine graindifferentiation of probability weights associated with preferreddescriptive terms within the segment. The coarse grain segment hasrelatively coarse grain differentiation of probability weightsassociated with preferred descriptive terms within the segment. Themethod includes, in response to receiving subsequent incremental inputentered by the user, selecting and ordering a collection of contentitems by promoting the ranking of content items associated with thelearned preferred descriptive terms of the user according to thedifferentiation provided by the segmented probability distributionfunction.

Under another aspect of the invention, the probability weights of thesegmented probability distribution function associated with preferreddescriptive terms are based on the frequency of selection of contentitems associated with said preferred descriptive terms.

Under a further aspect of the invention, the probability weights of thesegmented probability distribution function associated with preferreddescriptive terms are based on the recency of selection of content itemsassociated with said preferred descriptive terms.

Under yet a further aspect of the invention, the probability weights ofthe segmented probability distribution function associated withpreferred descriptive terms are based on the number of selections ofcontent items associated with said preferred descriptive terms.

Under an aspect of the invention, the segmented probability distributionfunction also has an overflow segment. The probabilities weights withinthe overflow segment are not differentiated from other probabilitiesweights within the overflow segment. Whereas the probabilities weightswithin the overflow segment are differentiated from the probabilityweights within the coarse and fine grain segments.

Under yet another aspect of the invention, the coarse grain segmentincludes at least two weight groups. Each weight group has a preselectedrange of probability weight values that determine which probabilityweights are in the weight group so that any probability weights in aparticular weight group are not differentiated from other probabilityweights in the same group. However, the probabilities weights indifferent weight groups are differentiated from each other.

Under a further aspect of the invention, each weight group includes ahigh probability weight value and a low probability weight valuedefining the preselected range of probability weight values. The highprobability weight value and the low probability weight value can beseparated by at least one order of magnitude. The probability weightsassociated with preferred descriptive terms can be integer values.

Under an aspect of the invention, the segmented probability distributionis stored on a user client device and selecting and ordering thecollection of content items includes selecting and ordering contentitems stored on the client device.

These and other features will become readily apparent from the followingdetailed description wherein embodiments of the invention are shown anddescribed by way of illustration.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

For a more complete understanding of various embodiments of the presentinvention, reference is now made to the following descriptions taken inconnection with the accompanying drawings in which:

FIG. 1 illustrates two modules of a leaning engine.

FIG. 2 illustrates a collections of signatures of a user.

FIG. 3 illustrates a collection of signatures of a user for a singledataspace in a single location.

FIG. 4 illustrates orthogonal periodicities.

FIG. 5 illustrates overlapping periodicities.

FIG. 6 illustrates sample vectors in three vector spaces.

FIG. 7 illustrates seminormalization of signature probabilities.

FIG. 8 illustrates an example of a seminormalized signature.

FIG. 9 illustrates an example of detecting an increased level ofactivity associated with a content item.

FIG. 10 illustrates a context-specific personal preference informationservice.

FIG. 11 illustrates the local tracking and strengthening of the personalpreference signatures based on user activity and the content on a mobiledevice.

FIG. 12 illustrates the information flow when a user device makes arequest to a service provider.

FIG. 13 illustrates an alternate information flow when a user devicemakes a request to a service provider.

FIG. 14 illustrates examples of services that benefit from thecontext-sensitive personal preference service.

FIG. 15 illustrates possible user device configurations for use with thelearning system and the context-sensitive personal preference service.

DETAILED DESCRIPTION OF THE INVENTION

Preferred embodiments of the invention capture user preferences for asingle user or a family of users based on historical observations of theusers' activities and by use of a statistical learning model (alsoreferred to as a learning engine). In an illustrative embodiment, theusers of the family are members of a household using a single interfacedevice. The learning model identifies a signature, or set of signatures,for the entire household as a stochastic signature. This stochasticsignature is used to predict future activities in which members of thefamily may engage. For the sake of simplicity, the description willrefer to signatures and activities of a single user, when in fact, thesame applies to a group of users using a single interface device.

One benefit of the learning engine is to enhance the user's experienceby increasing the accuracy of the results of a user's query for contentand organizing these results so as to put the most likely desiredcontent items at the top of the results list. This increases the user'sspeed and efficiency in accessing the content he or she desires. Inaddition, the signatures can be used to identify clusters of users withsimilar interests for use with collaborative filtering techniques. Thelearning system can be one aspect of an overall system that providescontent and selectable actions to the user, or the learning system canbe a standalone system that monitors the user's actions across multiplesystems and provides learned user preferences to external systems basedon the context of the user's interaction with a particular externalsystem.

Information Captured by Signatures

The stochastic signature is a probabilistic model, and it is an identitythat can be used for validation, prediction, etc. While this type ofsignature can be used to identify a user according to his or herpreference, it is distinguished from a unique signature that a user mayemploy to authenticate an item or guarantee the source of the item, forexample. A stochastic signature may be created based on various types ofactivities, for example, watching television programs, dialing telephonenumbers, listening to music, etc. Thus, embodiments of the invention areuseful in a wide variety of contexts. In applications where there aremultiple dataspaces to be searched, the system will use a collection ofstochastic signatures related to that dataspace. For example, whensearching a personal address book, calendar, or air-line schedules, thesystem can use the set of signatures related to that particulardataspace. In addition, the system can also learn correlated activitiesacross dataspaces. This allows the system to learn how the userinteracts with related dataspaces and use that learned behavior topersonalize the presentation of content to the user. However, for thesake of simplicity, certain embodiments of the invention will bedescribed with reference to a single dataspace interface, e.g., atelevision system interface.

In the context of a user device with limited input capability, forexample, a television remote control, the stochastic signature isparticularly useful because it can be difficult and/or time consuming toenter queries on such a limited input device. The stochastic signatureincreases the likelihood that the desired search results will be foundbased on limited query input. For example, if a particular household hasgenerally watched a certain program at a given time in the past,stochastic signatures can be used to predict that the household willwatch the program at the given time in the future. Thus, instead ofrequiring a member of the household to enter the title of the program,the learning system can predict that the member wishes to watch theprogram based on only a few button presses.

Embodiments of the present invention build on techniques, systems andmethods disclosed in earlier filed applications, including but notlimited to U.S. patent application Ser. No. 11/136,261, entitled Methodand System For Performing Searches For Television Programming UsingReduced Text Input, filed on May 24, 2005, U.S. patent application Ser.No. 11/246,432, entitled Method And System For Incremental Search WithReduced Text Entry Where The Relevance Of Results Is A DynamicallyComputed Function of User Input Search String Character Count, filed onOct. 7, 2005, and U.S. patent application Ser. No. 11/235,928, entitledMethod and System For Processing Ambiguous, Multiterm Search Queries,filed on Sep. 27, 2005, the contents of which are hereby incorporated byreference. Those applications taught specific ways to performincremental searches using ambiguous text input and method of orderingthe search results. The present techniques, however, are not limited tosystems and methods disclosed in the incorporated patent applications.Thus, while reference to such systems and applications may be helpful,it is not believed necessary to understand the present embodiments orinventions.

FIG. 1 shows the architecture of an illustrative learning engine 100.There are two distinct modules to learning engine 100—a data collectionmodule 105 and a signature computation module 110. Data collectionmodule 105 monitors the user activity for channel tuning, DVR recording,etc. and captures the relevant statistics of the activity, for example,the duration a TV channel was watched, as well as the genres andmicrogenres (discussed below) of the program that was watched. In thecase of a mobile device, additional information is collected, such asthe type of dataspace being visited (e.g., phone book, calendar, anddownloadable media content), geographic location of the mobile device,etc. Data collection module 105 can reside in a client device, where itgathers data about the users' activities and sends this data tosignature computation module 110. In the alternative, data collectionmodule 105 can reside on a remote server that serves content to theclient device. In this case, the remote server collects data about thecontent requested by the users and passes this data to computationmodule 110.

As mentioned, the learning engine gathers information about channels,genres, and microgenres that the user has watched. Herein, the term“channel” refers to a tunable entity in a television system. A channelcan be identified by its name (CBS, ABC, CNBC, etc.).

The term “genre” refers to the overall theme of an item. In somesystems, every retrievable item is categorized into a genre. Thecollection of genres is system-definable, and can be as coarse or asfine-grained as necessary. In addition, the genres can be definedindependent of the retrievable items and can be defined ahead ofpopulating a content system with retrievable items. In oneimplementation, a function g(x) returns a subset of the set of genresfor a given item. Thus, g(x) is a function with a domain space of a setof retrievable items and the range space of a set of all subsets ofgenres. This is so because any retrievable item may belong to more thanone genre, e.g., a movie Sleepless in Seattle has a genre of movie andromance.

The term “microgenre” refers to a very refined, unambiguous theme ofdescriptor for a given item. For example, New England Patriots as asearch item has a microgenre of NFL Football and genres of football andsports. As with genres, a search item can have multiple microgenres.While genres are “macro” themes, microgenres are “micro”, unambiguousthemes; these themes come from descriptive terms and metadata within thesearch items. Thus, the microgenres for New England Patriots alsoinclude Tom Brady. Microgenres are not limited to a set of predetermineddescriptors, as are genres in the prior art, but can be any word thatdescribes the item. Whether a particular theme is a genre or microgenredepends on the particular item with which the theme is associated andthe configuration of the content system. Thus, microgenres are dynamicand generated “on-the-fly”, while genres are static and system defined.

In dataspaces other than the television content space, the channel,genre, and microgenre approach to characterizing items is modified toreflect the attributes of the content items in that particulardataspace. Thus, for a telephone directory dataspace, the channelstatistics are replaced with statistics related to the person or entitycalled. The genre statistics are replaced by statistics related to thetype of entity called, for example, individual or business. While themicrogenre statistics are replaced by statistics related to keysecondary attributes of the item, such as home, office, and mobiletelephone numbers as well as, for example, telephone numbers of personsrelated to the persons called.

Computational module 110 is sent the current day's data periodically anddetermines the users' signatures. In so doing, computational module 110combines this current data with historical data using exponentialsmoothing or other smoothing techniques (discussed below) so that thesignatures adapt over time to the users' changing preferences.Computational module 110 also performs other computations involving thesignatures, for example, combining the individual signatures to obtainaggregate signatures that predict the viewing preferences of a largecollection of individuals or creating signatures that capture theaverage activity level associated with a particular program (describedin greater detail below). In one embodiment of the system, computationalmodule 110 resides in one or more servers, to exploit the computationalpower of larger processors. However, in some implementations, e.g.,where privacy is an issue, computational module 110 may reside in theclient device.

A particular stochastic signature is a normalized vector ofprobabilities. The probabilities capture the historical patterns of theuser's behavior with respect to a particular set of activities. Anexample of a signature for use with a television system is {(ABC 0.3),(CBS 0.2), (NBC 0.5)}. This signature captures that over a given timeperiod, when the user was watching television, the user watched ABC 30%of the time, CBS 20% of the time, and NBC 50% of the time. Thestochastic nature of the signature says that this is a historicalaverage and not an exact number.

Because the system captures the user's behavior and preferences acrossmultiple dataspaces, taking into account the geographic location of theuser, or the user's client device, the multiple signatures can berepresented as a set with three indices. Thus, the conventionsignature(t, g, s) represents a signature in geographic location g attime t for dataspace s. This allows the system to use differencesubspace projections to utilize the information contained in the entireset. For example, the system may utilize the user's preferences based onactivity across all geographic locations or based on a composite ofmultiple times for a given dataspace and given location. The compositesignature is described in greater detail below.

Although time is obviously a continuous variable, for the purpose oflearning the user's preferences and activities, a coarse, ordiscretized, view of time is used to capture all activity. Thus, thesystem divides time into discrete quantities and represents time as aninteger from one to the number of discrete quantities in a day. Forexample, time can be divided into the number of minutes in a day,thereby representing time as a number 1 to 1440. In addition, thisdiscrete representation of time can be further subdivided into timeslots that encompass multiple minutes, as discussed below. The duration,and therefore the number, of time slots is selected based on the natureof the activity of the particular dataspace. For example, in thetelevision dataspace it is appropriate to divide time into 30-minutetime slots to correspond to the program boundaries. In other dataspaces,the coarseness can vary. Although it is not necessary to have the sametime division in all dataspaces, the examples set forth below assumeidentical time slot durations for the sake of simplicity. Similarly,geographic location, though continuous, is discretized and representedby character strings. For example, the geographic location identifierscan be a postal code, a major metropolitan area, or an area of a givensize with the latitude and longitude of its center being the locationidentifier.

There are many possible collections of signatures that capture theactivities of the user or family of users at various levels ofgranularity. FIG. 2 shows a sample signature hierarchy for the multipledataspace learning model, with n locations 200, m dataspaces 210, and ktime slots 220. At the first level, the figure illustrates activities ineach location 200. Within each location 200, the system capturesdataspace-specific activities in individual signatures. Inside eachdataspace 210, for each time slot 220, the system obtains a uniquesignature. Finally, the signature hierarchy captures the nature of theactivity within the time slot by appropriate keyword 230, genre 240, andmicrogenre signatures 250 (or equivalent statistics depending on thedataspace, as described above). The illustrative learning system shownin the figure has 3 nmk signatures in the collection.

The timeslots shown in FIG. 2 can be further divided according to theparticular needs of the learning system. Thus, a top-level time slot canhave lower level time slots organized beneath the top-level time slot.For example, a top-level time slot can be a day organized intolower-level time slots of an hour or half-hour increments, each havingits own collection of signatures. Similarly, the day time slot can havea collection of composite signatures beneath it that aggregate all ofthe information of the individual time slots for that given day into asingle composite signature.

FIG. 3 shows an illustrative example of the organization of a signaturecollection 300 for the user in the television program dataspace at asingle location. At the top level, the signatures are classified intovarious periodicities for the user, as discussed in greater detailbelow. The example in FIG. 3 shows a weekday periodicity 305 and aweekend periodicity 310. Within each periodicity, signature collection300 is further divided into individual time slots 315 with a composite320 for each day. Within each further division exists three types ofsignatures: channel 325, genre 330, and microgenre 335. Thus, there isone of each of these three types of signatures for every weekday timeslot, weekend time slot, and one for each weekday composite and weekendcomposite. Therefore, the system captures the activities performed bythe user in this single dataspace and at this single location as definedby the hierarchy present in signature collection 300.

Because activities vary widely in a multiple dataspace environment, thesystem can capture the user's activities, according to the signaturehierarchy, as duration and/or count. In other words, the system cantrack the amount of time the user spent performing an activity, or thesystem can track the number of times the user preformed a particularactivity, or the system can record both. For example, if the system ismodeling a DVR recording activity or DVD ordering activity, there is noduration measure associated with it. Thus, in these cases, the systemwill capture the intensity of the activities by the count (frequencies)of the activities. However, other activities have duration as a naturalmeasure of the intensity of the activities (e.g., watching a televisionprogram). While still other activities have both count and duration as anatural measure of intensity (e.g., placing a telephone call andconducting the call). To be inclusive of all activities, the systemmodels every activity by both count and duration. Thus, there are twosignatures for each keyword, genre, and microgenre division of thehierarchy. Likewise, there are two for each composite as well. For eachtime, location, and dataspace, a function ƒ defines the convolution ofthe two intensity measures into a single signature:f _(tgs): (count, duration)->single measure   (Equation 1)

For the sake of simplicity, this description omits the adjective, countor duration, in referring to signatures, opting for disambiguation basedon the context.

In one embodiment of the invention, signatures capture the televisionviewing activity of the family of users in a single geographic location,and these signatures are used to identify and organize televisionprogram search results. The learning engine divides a day into timeslots. For the purposes of this example, there are 48 time slots in a24-hour day. Thus, one time slot corresponds to the smallest lengthTV-program, i.e., 30 minutes. In other implementations, time slots maybe larger or smaller, for example, in a system using stochasticsignatures to identify a user's telephone calling preferences, the timeslots may be two to three hours long. During each time slot, the useractivity is recorded and the learning system creates a time slotsignature for each time slot. In addition, at the end of each day, thelearning system creates a composite signature based on the datacollected across all time slots within the current day. The signature issaid to be composite in that it represents a user's activity acrossmultiple time slots. As discussed in greater detail below, the learningsystem uses smoothing techniques to create evolving signatures thatretain activities in the distant past as well as the most recentactivities.

The day is divided into time slots because each family of users has arecurring viewing behavior based on the time of day. Thus, the learningsystem learns television-viewing preferences from the past behaviorduring a given time slot. Any queries for television content thatoriginate in that time slot on a later day can use these preferences toidentify and organize content by using that time slot's signature.

For example, in an illustrative family of three—husband, wife, and achild—mornings and afternoons are taken up by soap operas and talkshows; afternoons are taken up by cartoons and children's programming;and evenings are taken up by business, movies, and prime time shows.During these periods, it is likely that queries for current televisioncontent also relate to the corresponding past viewing behavior. Thus,signatures that capture this past behavior are used to identify andorganize content consistent with this past behavior. However, for moreaggregate behavior, independent of time slots, it is desirable to have acoarse grain view of the day's activity in the household. The time slotactivity is aggregated into a day's activity; this is the basis of thecomposite signature. Thus, at the end of each day, the system hascollected and aggregated 49 different signatures, 48 individual timeslot signatures and one composite signature.

Composite signatures serve two purposes. First, if the family of usershas a slight time-drift in their behavior (e.g., some days a particularuser watches a given program at 10:00 AM, while other days at 10:45 AM),the time slot signatures may get shifted by one slot. However, thecomposite will still capture the overall viewing behavior correctly.Second, a particular user may time-shift deliberately by many timeslots. The composite signatures will also correctly capture thisbehavior.

User Periodicity

The above example implicitly assumes that the user has a recurringbehavior with a periodicity of a day. However, the learning system mayutilize other periodicities, as explained below. As mentioned above, onebenefit of the learning system is to enhance the user's experience byincreasing the accuracy of the results of a user's query for content andorganizing these results so as to put the most likely desired contentitems at the top of the results list. This increases the user's speedand efficiency in accessing the content he or she desires.

Towards this end, the learning system infers periodicity of activities.For example, as discussed above, there is a daily periodicity ofactivities. However, the daily periodicity model may not always apply,as occurs during a weekend, during which time the users' activities canbe quite different from those during the week. To capture this differentbehavior pattern, for example, the system will utilize two differentperiodicities. Thus the weekday periodicity will contain data for thedays during the week, while the weekend periodicity will be empty forthose days and vice versa. This is an example of orthogonal periodicity.

The term orthogonal refers to the fact that the periodicity waveformsare orthogonal; i.e., if f(x) is a periodicity function and g(x) isanother periodicity function, then f(x) and g(x) have orthogonalperiodicity iff(x)g(x)=0; 0≦x≦∞  (Equation 2)

Equation 2 defines strong orthogonality, or pointwise-orthogonality, incontrast with the strict mathematical definition of orthogonality offunctions (see F. B. Hildebrand, Introduction to Numerical Analysis,second edition, McGraw-Hill Book Company, New York, 1974, herebyincorporated by reference). FIG. 4 illustrates an example of orthogonalperiodicity. The figure shows a variety of waveforms that represent theactivity captured by a set of signatures for a particular dataspace in aparticular location. The Y-axis is the intensity of activity during aparticular day, and X-axis is the day. A weekday waveform 405 capturesthe activity during the days of the week (i.e., Monday-Friday). Whereasa Saturday waveform 410 and a Sunday waveform 415 capture the activityon Saturday and Sunday, respectively. A solid line shows weekdayperiodicity waveform 405; a short dashed line shows the Saturdayperiodicity waveform 410; and a long dashed line show Sunday periodicitywaveform 415.

FIG. 4 illustrates the waveforms are orthogonal in that the activitylevel for weekday waveforms is zero during Saturday and Sunday, whilethe Saturday waveform is zero for all non-Saturday days, and the Sundaywaveform is zero for all non-Sunday days. The system captures theseorthogonal periodicities by storing the activities in distinct sets ofsignatures, with one set of signatures for each orthogonal period. Asexplained above, the set can include both individual time slotsignatures as well as a composite signature for the entire orthogonalperiod. When a user query is submitted within a particular period, thecorresponding set of signatures is used in identifying and organizingthe search results.

Although the above example is in terms of a week, periodicity can extendbeyond a week. For example, periodicities can exist within a day or canextend beyond a week. In addition, some periodicities may not beorthogonal. Thus, the system uses a second kind of periodicity, namelyoverlapping periodicity, to capture this phenomenon.

In overlapping periodicities, the periods overlap; i.e., the same timeand day can belong to multiple periods, one having a larger frequencythan the other. Thus, the strong orthogonality property of Equation 2does not apply to overlapping periodicities. FIG. 5 shows an example ofoverlapping periodicity. In this example, a user watches a recurringprogram every Wednesday, along with the usual daily programs that shewatches. Thus, there is a weekly period 505 with a frequency of once perweek and a daily period 510 with a frequency of once per day.

Overlapping periodicities are distinguished by storing the sameactivities in multiple sets of signatures, one set for each overlappingperiodicity. In the example of FIG. 5, the system will store the sameWednesday activity both in daily set 510 and weekly set 505. Notice thatweekly set 505 does not contain activities from other days. When a queryis submitted on a Wednesday, a union of both signatures is used inidentifying and organizing the content results. Both signatures arecombined in such as way as to reflect the fact that the weekly signature505, anchored on Wednesdays, has a greater impact on the query resultsthan does daily signature 510.

As mentioned above, the learning system defines periodicities accordingto the users' behavior. In one illustrative implementation, the systemcompares each recorded user action and determines the periodicity ofsimilar actions by measuring the time that elapses between the similaractions. The similarity can be based on, for example, the user watchingthe same television channel or the user watching television programs ofthe same genre or microgenre. Therefore, if a user watches a particulartelevision show on the first Tuesday of every month, the system wouldcapture this as a monthly periodicity. Thus, although the system can usepredefined periodicities, the system creates periodicities of any timeduration as needed by the particular usage case. As mentioned above,capturing the user's viewing preferences in the television dataspace isonly one example of an implementation of the learning system. Otherexamples include learning the user's dialing preferences in thetelephone dataspace or tracking the user's buying behavior in theinternet dataspace.

Signatures as Multiple Vectors

As explained above, vectors can be used to capture the family of users'behavior. A vector is defined as a linear array of numbers. Every vectorbelongs to a vector space. In one embodiment of the learning system, thesystem only operates in vector spaces in R^(+n), defined asR ^(+n)={(x ₁ , x ₂ , . . . , x _(n))|x _(i)≧0 all i}  (Equation 3)

The dimensionality of the vector space depends on the type of vector.For example, the dimensionality of a channel vector space is the numberof channels in the television system. The values in the vector alsodepend on the type of vector; e.g., it can be duration or count or anyother metric deemed appropriate to capture the activity of the family ofusers. FIG. 6 shows an example of a collection of vectors capturing theusers' activity between 10:00 AM and 10:30 AM on a weekday.

The vectors in FIG. 6 correspond to three different vectorspaces—channel, genre, and microgenre. The dimensions of these vectorspaces are the number of channels in the TV system, the number of genresdefined in the learning system, and the number of microgenresdynamically created in the learning system, respectively. Only nonzerovalues are stored in the various vectors. All other values areimplicitly zero and are not stored in the system. Thus, the learningsystem fundamentally stores all vectors as sparse vectors. Thetechnology of sparse vectors and sparse matrix computations eases theburden of working with large vector spaces (see I. S. Duff, A. M.Erisman, and J. K. Reid, Direct Methods for Sparse Matrices, Monographson Numerical Analysis, Oxford Science Publications, Clarendon Press,Oxford, 1986, for a description of numerical computations using sparsematrices and sparse vectors, hereby incorporated by reference).

A channel vector 605 in the figure has nonzero values for channels CBS,ABC, and CNBC. The values correspond to the number of minutes the userwatched each channel between 10:00 AM and 10:30 AM. Similarly, theprogram genres are captured in a genre vector 610. In this example, theCBS and CNBC channels were broadcasting programs of type business andABC was broadcasting a program of type comedy. Finally the programmicrogenres are captured in a microgenre vector 615. In the aboveexample, ABC was broadcasting the comedy show Seinfeld, CNBC wasbroadcasting a business show Squawkbox, and no microgenre was createdfor the CBS show.

As previously mentioned, the techniques described above can beimplemented in data collection modules and signature computation modulesthat reside on either a client device or a remote server system. Thus,the channel, genre, and microgenre data can be gathered and processedlocally by the client device, or this information can be sent to aremote server system for processing. Likewise, the signatures can resideon a client device or on a remote server system for use as describedbelow.

In addition to capturing the user's activities according to keyword(i.e., channel in the television dataspace context), genre, andmicrogenre, the system also learns the amount of time the user spends ineach dataspace independent of location and time slot. This gives rise toyet another signature: the dataspace fraction signature. The dataspacefraction signature (herein “dfs”) has the coordinates of time andlocation and is represented by dfs(t, g). The signature dfs(t, g) is anormalized probability vector indicating the fraction of time (and/oractivity count) the user spent in various dataspaces. For example,dfs(t, g)[s] contains the value indicating the fraction of time and/orcount spent in dataspace s, at time t in location g. This two-coordinatesignature is used to reorder the search results space when a searchacross dataspaces is performed. Meanwhile, as described above, athree-coordinate signature is used to reorder the items within eachdataspace, e.g., ks(t, g, s) denotes a keyword signature in time slot t,in location g, and in dataspace s; ks(t, g, s)[x] denotes the value ofelement x in the keyword signature. Therefore, when the user initiates asearch across all dataspaces, the system will reorder content items fromthe multiple dataspaces according to the user's dataspace preferencesbased on the information contained in the dataspace fraction signature.If the user performs actions in one particular dataspace relative toanother, the result from the more heavily used dataspace would bepromoted over the results from the lesser-used dataspace.

The following example is provided to illustrate this aspect of thelearning system. A mobile user visited the telephone dataspace 30 times,the television dataspace 20 times, and the web dataspace 10 times whilelocated in Denver during the 10 AM time slot. During these interactionswith the system, the user called Sam, Sally, and Stewart, speaking for5, 15 and 10 minutes respectively. The user watched a television programentitled “Seinfeld” for 30 minutes. In addition, the user browsed theGoogle webpage for 10 minutes and Yahoo! webpage for 20 minutes,respectively. Using a count measure for the dataspace fraction signatureand a duration measures for the television, telephone, and webdataspaces, the keyword signature and dataspace fraction signatureensemble, will be as follows:

-   -   dfs(10, “Denver”)[“phone-space”]=0.5    -   dfs(10, “Denver)[“TV-space”]=0.33    -   dfs(10, “Denver”)[“web-space”]=0.17    -   ks(10, “Denver”, “phone-space”)[“Sam”]=0. 17    -   ks(10, “Denver”, “phone-space”)[“Sally”]=0.50    -   ks(10, “Denver”, “phone-space”)[“Stewart”]=0.33    -   ks(10, “Denver”, “TV-space”)[“Seinfeld”]=1.0    -   ks(10, “Denver”, “web-space”)[“Google”]=0.33    -   ks(10, “Denver”, “web-space”)[“Yahoo!”]=0.67

Thus, if the user enters a text query starting with the letter “S”, allresults beginning with the letter “S” would be presented to the user.However, the matching results from the phone-space would be promotedover the results from the TV-space and the web-space because thedataspace fraction signature probability for the phone-space is thegreatest. This is so even though the probability for the lone TV-spaceitem is greater than any of the phone-space items. Within thephone-space, the individual items would be sorted according to thekeyword signature probability values. Therefore, the entry for “Sally”would be promoted over the other phone-space items. This example clearlyshows the Bayesian property of the signatures. That is, theprobabilities add up to one, conditioned on the fact that the user is ina particular dataspace (see B. W. Lindgren, G. W. McElrath, D. A. Berry,Introduction to Probability and Statistics, Macmillan publishing co.,New York, 1978, herein incorporated by reference, for more details onBayes's theorem).

As described above, the signatures associated with a particulardataspace (i.e., keyword, genre, and microgenre signatures) capture theprobability of the user performing a future action or desiring a futurecontent item based on past activities and selections that took placewithin that particular dataspace. Thus, the individual dataspacesignatures are conditional signatures in that the probabilities theymeasure are conditioned upon the user operating in the particulardataspace to which those signatures relate.

The dataspace fraction signature probabilities can be used to weight theindividual dataspace signature probabilities to provide a measure of theprobability of a user performing a given action outside of a particulardataspace. This operation gives rise to an unconditional signature. Theunconditional signature measures the probability of the user performingan action outside of any particular dataspace based on the informationcontained in the individual dataspace signatures and the informationcontained in the dataspace fraction signatures. The system uses Equation4, below, to determine the unconditional keyword signature for anactivity “A”. Unconditional signatures for genre and microgenre can bedetermined in the same way.uks(t, g, s)[A]=ks(t, g, s)[A]*dfs(t, g)[s]  (Equation 4)

The learning system can organize the various dataspaces, content, andselectable actions into a tree hierarchy, which the user can navigateusing the client device. The unconditional signatures are used by thelearning system to rearrange the various branches of the tree structureso as to present the most favored content to the user based on theprobabilities stored in the signatures. In addition, the unconditionalprobabilities enable the system to present lists of commingledselectable actions and content items based on the most commonlyperformed actions and most commonly exhibited preferences. For example,the learning system is capable of creating a “My Favorites” list basedon the various signatures, or the system could rearrange a content treehierarchy in order to reduce the effort required of the user to reachcertain preferred content.

Correlated Activity Signatures

The learning system is also capable of learning correlated activitiesacross dataspaces. A correlated activity is an activity performed in asecondary dataspace while starting from a primary dataspace. In general,by capturing correlated activities across dataspaces, the system islearning not only standalone actions and content preferences, but thesystem is learning chains of actions performed by the user. For example,a user enters the telephone dataspace of his device to make a telephonecall. During the telephone call, the user wishes to enter the calendardataspace to search for and review the date and time of a particularappointment. In this example, the user remains engaged with the primarydataspace, the telephone dataspace, for the duration of the telephonecall. The user also performs a correlated action, the act of searchingfor an appointment, in the secondary dataspace, which is the calendardataspace.

The purpose of learning the correlated activities is to achieve betterordering of the search query results in the secondary dataspace based onthe correlated activities learned by the system. Thus, the correlatedactivity signatures provide yet another way to learn the preferences ofthe user and how the user interacts with his or her client device. Thisadditional set of preferences and learned actions further enhances theuser experience.

In general, the system has an activity matrix A that is a square N by Nmatrix, where N is the number of dataspaces. Each entry in the matrix isa signature vector that captures the actions performed in the secondarydataspace while operating from the primary dataspace. Thus, A is in facta three dimensional matrix, which can be defined as follows:A(i, i)[x]:=0; 1≦i≦N, for all items x ∈ dataspace iA(i, j)[x]:=average number of accesses of item x in dataspace j while indataspace i; 1≦i≦N; 1≦j≦N; i≈j; for all items x ∈ dataspace j  (Equation 5)

The matrix determined by Equation 5 captures the correlated activitiesof the user, and therefore can be used in accordance with the techniquesdisclosed herein to predict the probability that the user would performan action in a secondary dataspace while operating from a primarydataspace. In addition, the correlated activity signatures can be usedto determine the unconditional probability of the user accessing akeyword item x in dataspace s, location g, and at time t. Theprobability determination depends in part on the mode of access utilizedby the user. As described in Equation 6 below, if the user is enteringdataspace s at the root level of the client device (i.e., the user isnot currently in a dataspace), the probability determination is based onthe dataspace fraction signature and the relevant signatures for theselected dataspace (e.g., the keyword signature, the genre signature, orthe microgenre signature). If the user is entering dataspace s fromanother dataspace, the probability determination is based on thedataspace fraction signature and the correlated activity matrix A.

$\begin{matrix}{{{Prob}\lbrack x\rbrack} = \begin{Bmatrix}{{{{{dfs}\left( {t,g} \right)}\lbrack s\rbrack} \star {{{ks}\left( {t,g,s} \right)}\lbrack x\rbrack}};} \\{s\mspace{14mu}{is}\mspace{14mu}{visited}\mspace{11mu}{at}\mspace{14mu}{root}\mspace{14mu}{level}} \\{{\sum\limits_{1 \leq i \leq N}\;{{{A\left( {i,s} \right)}\lbrack x\rbrack} \star {{{dfs}\left( {t,g} \right)}\lbrack i\rbrack}}};} \\{otherwise}\end{Bmatrix}} & \left( {{Equation}\mspace{14mu} 6} \right)\end{matrix}$

For the sake of simplicity, the learning system's ability to capturecorrelated activities was described in terms of a primary and secondarydataspace only. However, the invention is not limited to correlationsbetween only two dataspaces. The learning system can also capture useractivities and preferences when the user enters a first dataspace,enters and performs an action in a second dataspace, and enters andperforms yet further actions in a third dataspace. In fact, using theprinciples and techniques described above, the learning system cancreate N! number of correlation signatures.

Signature Clustering

As described above, the users' activity and preferences are stored asvectors. Capturing the users' activity and preferences as vectors hasmany benefits. Among these is the fact that because signatures arevectors, one can easily determine when two signatures are nearlyidentical. For example, let {tilde over (x)} and {tilde over (y)} be twosignatures in the same vector space. Lower case letters in bold willgenerally denote normalized probability vectors in appropriatedimension. A tilde over the top of a vector will generally denoteun-normalized vectors. Greek symbols and lower case letters, withoutbold, will generally denote scalars. If

$\begin{matrix}{{{{vector}\mspace{14mu}{{angle}\left( {\overset{\sim}{x},\overset{\sim}{y}} \right)}} = {\frac{{\overset{\sim}{x}\;}^{T}\overset{\sim}{y}}{{\overset{\sim}{x}}{\overset{\sim}{y}}} \geq \left( {1 - ɛ} \right)}},} & \left( {{Equation}\mspace{14mu} 7} \right)\end{matrix}$where ε is a small fraction in the vicinity of 0.01, then the twosignatures are nearly identical. Equation 7 states that if the cosine ofthe angle between the two signatures is small enough, then they arenearly identical, up to a scaling factor. The scaling factor recognizesthat two vectors may have different magnitudes, but still beoverlapping. For example, a first user has a genre vector of {(sports20); (world news 50)} and a second user has a genre vector of {(sports60); (world news 150)} where the first value within the vector elementsis the genre and the second value is minutes watched per day. Althoughthe magnitudes of these two vectors are different, the genres and ratioof minutes of sports to world news is identical. Thus, the learningsystem identifies these two signatures as nearly identical. The learningsystem can exploit this aspect in a variety of ways, as described ingreater detail below.

In one illustrative implementation, the system clusters signatures thatare similar into subsets. The subsets can then be used for manypurposes, such as promotions, targeted advertisements, etc. For example,if several users of a particular cluster have watched a certaintelevision program, this television program will be recommended to otherusers of the cluster who have yet to view the program. Similarly,outside of the television context, if users of a particular cluster havepurchased a given item, ads for this item are presented to the otherusers of the cluster who have not purchased the item.

The notion of viewing signatures as vectors can be exploited todetermine the similarity between the signatures by using Equation 7.Each cluster represents nearly identical signatures. Initially, theprocedure starts with singleton clusters, and recursively collapses themuntil no more merging is possible. An example of pseudo-code thatgenerates clusters of signatures is provided below:

PROCEDURE   Inputs: 1. N signatures s₁, s₂, ..., s_(N) 2. Tolerancethreshold ε, 0 ≦ ε ≦ 1.0   Outputs: 1. Sets Ψ₁ , Ψ₂ , ..., Ψ_(c)containing the signature clusters   BEGIN 1. Initially define singletonsets Ω_(j) := {s_(j) }; 1 ≦ j ≦ N 2. merged := FALSE 3. for 1 ≦ i ≦ N −1 do a. if set Ω_(i) = Ø, continue b. for i+1 ≦ j ≦ N do i. if set Ω_(j)= Ø, continue ii. If for every x ∈ Ω_(i) and every y ∈ Ω_(j)vector_angle(x, y) ≧ (1 − ε), then A. Ω_(i) := Ω_(i) ∪ Ω_(j) B. Ω_(j) :=Ø C. merged := TRUE end_if end_for end_for 4. if merged = TRUE, go tostep 2. 5. c := 0 6. for 1 ≦ i ≦ N do a. if Ω_(i) ≠ Ø then i. c := c + 1ii. Ψ_(c) := Ω_(i) end_if end_for   END END_PROCEDURE

Signature Decomposition

In addition, the learning system can decompose one or more signatures toapproximate the number of family members in a signature, the gender ofeach family member, and the ages of the family members. The system usesa nonlinear optimization model to approximate these values. In thisexample, the learning system uses genre signatures; similar models applyto channel and microgenre signatures.

The technique starts with historical, human behavioral, statistical dataon television viewing habits obtained from generally available data(such as the user viewing preferences available from the DVB Project,see www.dvb.org). In particular, Δ is a set of all genres that areavailable to the viewing public. Upper case Greek letters in bold fontgenerally denotes sets. Thus, a collection of probability distributionsexists, namelyf _(g) ^(y)(t)≡Probability that a genre g would be watched by a personof gender y and age t; y={male, female};0≦t≦∞; g ∈ Δ  (Equation 8)

The learning system also provides a signature s that collectivelydescribes the viewing habits of the household. The illustrative maximumlikelihood estimation problem formulated below defines the most likelyset of household members that may produce the signature s. For thepurposes of this example, all vectors, normalized or unnormalized, arelower case bold letters.

The inputs to the optimization problem are the probability distributionfunctions f_(g) ^(y)(t) and a family signature s that collectivelyrepresents the activities of a household. The outputs are n, the numberof members in the household, where 1≦n, the age and gender of eachfamily member i, where 1≦i≦n, and a set of signatures s₁, s₂, . . . ,s_(n), where signature s_(i) corresponds to family member i. Further,let N=|Δ|, the cardinality of the Genre set, Φ=set of all nonnegativebasis matrices B for the vector space R^(+N)(i.e., B=[b₁, b₂, . . . ,b_(N)], where b_(i) is a nonnegative N-vector and b_(i) 1≦i≦N, arelinearly independent, and for any vector s ∈ R^(+N),

${s = {\sum\limits_{i = 1}^{i = N}\;{\alpha_{i}b_{i}}}},$with α_(i) ≧0).

The decision variables are as follows: basis matrix B ∈ Φ, variables x₁,x₂, . . . , x_(N), which represent the ages of family members withsignatures corresponding to the basis vectors in B, and variables y₁,y₂, . . . , Y_(N), which represent the gender of the correspondingfamily members.

For the purpose of optimization, it is necessary to define an objectivefunction to maximize. Towards this end, the system uses an intermediatefunction, as follows:

$\begin{matrix}{{{s = {\sum\limits_{i = 1}^{i = N}\;{\alpha_{i}b_{i}}}},{\alpha_{i} \geq 0},{{{for}\mspace{14mu}{any}{\mspace{11mu}\;}{{vecto}r}\mspace{14mu} s} \in R^{+ N}}}{{{and}\mspace{14mu}{any}\mspace{14mu}{basis}\mspace{14mu}{Matrix}\mspace{14mu} B} \in \Phi}} & \left( {{Equation}\mspace{14mu} 9} \right) \\{{{h\left( {v,x,y} \right)}\overset{\Delta}{=}{\prod\limits_{1 \leq k \leq N}\;{{f_{k}^{y}(x)}v^{(k)}}}},\text{}{{where}\mspace{14mu} v^{(k)}\mspace{14mu}{is}\mspace{14mu}{the}\mspace{14mu} k^{th}\mspace{14mu}{component}\mspace{14mu}{of}\mspace{14mu}{vector}\mspace{14mu} v}} & \left( {{Equation}\mspace{14mu} 10} \right)\end{matrix}$

Function h(v, x, y) evaluates the likelihood probability of a personwith age x and gender y having a signature v. Note that the system istaking the product of all the components of vector v. Thus, the maximumlikelihood estimation becomes

$\begin{matrix}{\underset{B \in \Phi}{Maximize}{\sum\limits_{1 \leq j \leq N}\;{h\left( {{\alpha_{j}b_{j}},x_{j},y_{j}} \right)}}} & \left( {{Equation}\mspace{14mu} 11} \right)\end{matrix}$subject to the following constraints:

$\begin{matrix}{{s = {\sum\limits_{i = 1}^{i = N}\;{\alpha_{i}b_{i}}}};{\alpha_{i} \geq 0};} & \left( {{Equation}\mspace{14mu} 12} \right) \\{{1 \leq x_{j} \leq \infty};{1 \leq j \leq N};} & \left( {{Equation}\mspace{14mu} 13} \right) \\{{y_{j} = \left\{ {0,1} \right\}};{1 \leq j \leq {N.}}} & \left( {{Equation}\mspace{14mu} 14} \right)\end{matrix}$

This optimization problem can be shown to be NP-Hard (see M. R. Garey,and D. S. Johnson, Computers and Intractability A Guide to the theory ofNP-completeness, W. H. Freeman and Company, New York, 1979, hereinincorporated by reference), since any technique needs to search over thespace of all bases in R^(+N) and the fact that the y variables areinteger variable. This problem has some similarities to anothernon-standard class of optimization problems known in the literature assemi-definite programs. An approximate solution to this problem can beachieved using an illustrative technique described below.

The estimation technique uses a first approximation by converting thesediscrete variables to continuous variables with bounds. This makes thetransformed problem amenable to differentiable optimization. Thetechnique also uses a second approximation by identifying a subset ofthe set of all bases Φ as the set of bases in R^(+N) that arepermutations of the coordinate basis matrix and restricts the search tothis set. Given any basis from this set, the inner iteration involves asteepest ascent technique in variables (α_(j), x_(j), and z_(j)) toobtain a local maximum (where z_(j) is a continuous approximation ofy_(j)). The iterations are terminated when no improvement in theobjective function occurs. After termination, the gender variables thatare fractional are rounded/truncated using a rounding heuristic,described below. Given a fixed basis matrix, the transformed maximumlikelihood estimation problem becomes a continuous maximum likelihoodestimation problem and is given by the following equations:

$\begin{matrix}{{Maximize}{\sum\limits_{1 \leq j \leq N}\;{h\left( {{\alpha_{j}b_{j}},x_{j},z_{j}} \right)}}} & \left( {{Equation}\mspace{14mu} 15} \right)\end{matrix}$subject to the following constraints:

$\begin{matrix}{s = {\sum\limits_{1 \leq j \leq N}\;{\alpha_{j}b_{j}}}} & \left( {{Equation}\mspace{14mu} 16} \right) \\\begin{matrix}{1 \leq x_{j} \leq \infty} & {1 \leq j \leq N}\end{matrix} & \left( {{Equation}\mspace{14mu} 17} \right) \\\begin{matrix}{0 \leq z_{j} \leq 1} & {1 \leq j \leq N}\end{matrix} & \left( {{Equation}\mspace{14mu} 18} \right) \\\begin{matrix}{\alpha_{j} \geq 0} & {1 \leq j \leq N}\end{matrix} & \left( {{Equation}\mspace{14mu} 19} \right)\end{matrix}$

An example of pseudo code for solving the above continuous maximumlikelihood estimation problem is given below. The pseudo code consistsof an outer iteration and inner iteration. In the outer iteration, thecode iterates through basis matrices. While in the inner iteration, thecode employs the steepest-ascent optimization technique to obtain theoptimal solution, given a basis matrix.

The steepest-ascent optimization technique has three steps. First, theoptimization technique obtains the gradient of the objective function atthe current iteration. This is done in step 2.c.ii, set forth below,using difference approximations. The technique of using differenceapproximations, as well as other numerical and matrix techniques can befound in D. G. Luenberger, Linear and Nonlinear Programming, secondedition, Addison-Wesley publishing company, Reading Mass., 1989, hereinincorporated by reference. Second, the optimization technique projectsthe gradient onto the null space of B, to obtain the ascent direction d(step 2.c.iii). Third, the optimization technique obtains the optimalstep length along d (step 2.c.iv). In the field of optimization, this iscalled a step-length computation and involves a one-dimensionaloptimization. The inner iterations proceed until no more improvement inthe objective function is possible. After this, the basis matrix ischanged and the inner iterations are reinitiated. Finally, roundingheuristics (such as those in G. L. Nemhauser, and L. A. Wolsey, Integerand Combinatorial Optimization, John Wiley & sons New York, 1988, hereinincorporated by reference) are employed to round off the fractionalvariables.

In the pseudo code set forth below, I is an identity matrix of order N,and P_(i) is the i^(th) permutation matrix in the sequence ofpermutations of the index set {1, 2, 3, . . . , N}.

PROCEDURE Inputs: 1. The historical probabilities f_(k) ^(y)(x) 2. Thefamily stochastic signature s Outputs: 1. Number of family members 2.Sex of family members 3. Age of family members 4. Individual signaturesof family members BEGIN 1. Initialize: a. Current basis matrix B := I b.Iteration counter i := 0 c. Permutation matrix P := I d. newOuterObj :=0; oldOuterObj := −∞ e. α_(j) := s_(j)  1 ≦ j ≦ N f. x_(j) := 1   1 ≦ j≦ N g. z_(j) := 0   1 ≦ j ≦ N h. stopping tolerance for inner iterationε := 0.001 i. stopping tolerance for outer iteration β := 0.0001 2.While ((newOuterObj - oldOuterObj) / |oldOuterObj| > β) Do    //outeriteration a. oldOuterObj := newOuterObj b. Initialize inner iteration;newInnerObj := 0; oldInnerObj := −∞ c. while ((newInnerObj -oldInnerObj) / |oldInnerObj| > ε) Do i. oldInnerObj := newInnerObj ii.Compute the gradient vector g := [∂h/∂α_(j)], [∂h/∂x_(j)],   [∂h/∂z_(j)]using difference approximation. iii. Project the gradient vector g on tothe null space of   matrix B to obtain the direction vector d := g_(⊥B)iv. Compute the optimal step length δ along the direction d.${v.\mspace{14mu}\begin{bmatrix}\alpha_{j} \\x_{j} \\z_{j}\end{bmatrix}}:={\begin{bmatrix}\alpha_{j} \\x_{j} \\z_{j}\end{bmatrix} + {\delta\; d}}$${{vi}.\mspace{14mu}{newInnerObj}}:={\sum\limits_{1 \leq j \leq N}{h\left( {{\alpha_{j}b_{j}},x_{j},z_{j}} \right)}}$endWhile d. i := i + 1; set P_(i) := Next permutation matrix in thesequence e. B := P_(i)IP_(i) ^(T) f. oldOuterObj := newOuterObj g.newOuterObj := newInnerObj endWhile 3. Use rounding heuristics to setfractional z_(j) variables to the   nearest integer value to obtainvariables y_(j). 4. Compute n := number of α_(j) that are greater than0. 5. Output the optimal solution: h. Output n as the number of familymembers i. For 1 ≦ j ≦ N Do i. if α_(j) > 0 then a. output α_(j)b_(j) asthe signature of person j b. output x_(j) as the age of person j c.output y_(j) as the sex of person j EndIf EndFor END END PROCEDURE

Signature Aging

The learning system also provides for remembering past behavior andintegrating it into the current signature. The fundamental reason forremembering the past is to infer a recurring pattern in the userbehavior, and use the inference to aid in future navigation. Exponentialsmoothing is a way of gradually forgetting the distant past, whilegiving more relevance to the immediate past. Thus, activities doneyesterday are given more relevance than activities done two days ago,which in turn is given more importance than the activities done threedays ago, and so on (see V. E. Benes, Mathematical Theory of ConnectingNetworks and Telephone Traffic, Academic Press, New York, 1965 foradditional information, herein incorporated by reference). Thistechnique has the added advantage of reducing the amount of computermemory consumed by the signature.

The learning system uses the concept of exponential smoothing in thecontext of learning the user's activities. For example, a set ofactivities for today is captured in a signature s, whereas all of thepast activities are remembered in a signature s* (s* remembers all ofthe past, since the recurrence relation, given below, convolutes all ofthe past into a single signature). At the end of the day (when thepresent becomes the past), the system updates s* by the recurrencerelations*=αs+(1−α)s*0≦α≦1   (Equation 20)

In Equation 20, α is called the smoothing parameter and it controls howmuch of the past the system remembers, and how fast the pastdecays—larger the α, faster the decay. Expanding the above recurrencerelation into a recurrence equation illustrates the machinery ofexponential smoothing. Where s*^((n)) denotes the past signature after ndays and s^((n)) represents the activities done during the n^(th) day.Equation 20 expands into

$\begin{matrix}{s^{*{(n)}} = {s^{(n)} + {\left( {1 - \alpha} \right)s^{({n - 1})}} + {\quad{{\left( {1 - \alpha} \right)^{2}s^{({n - 2})}} + {{\ldots\left( {1 - \alpha} \right)}^{({n - 1})}s^{(1)}} + {\frac{\left( {1 - \alpha} \right)^{(n)}}{\alpha}{s^{(0)}.}}}}}} & \left( {{Equation}\mspace{14mu} 21} \right)\end{matrix}$

Because α is ≦1, Equation 21 clearly shows that less weight is given tothe activities of that past. In some embodiments, all signatures foreach macro class (channel, genre, microgenre) are smoothed using theexponential smoothing technique. The determination of when to decay aparticular signature is based on the dataspace of the signature and thenature of activities performed in the dataspace. For example, in thetelevision dataspace, a decay period of one day is a suitable periodbecause television shows typically reoccur on a daily basis. Whereas thedecay period for the telephone dataspace would be longer so as to decayat a slower rate that the television dataspace. The decay parameter, orsmoothing parameter α, can be selected to control the degree of decay ofthe past user behavior.

The learning system also uses an adaptive decay technique to integratethe past signature information with the most recent signatureinformation. This adaptive decay technique is based on a hybridchronological-activity based smoothing and provides improved resultsover strict chronology-based aging when applied to signatures that takeinto account the user's geographic location. This technique enables theinfluence of past activities to decay over time, while still preservingthe past information during the user's absence from a particulargeographic location for a stretch of time. In general, the pastsignature will be decayed if (1) a new activity has occurred in thegeographic location and (2) the elapsed time since the last signaturedecay event is greater than a threshold. In essence, the system freezesthe signatures when no activity is happening in a given location,effectively stopping time for that location. When next an activityoccurs in that location, the system smoothes the signatures based onelapsed time.

If a traditional smoothing technique were employed to decay the memoryof the past once per day, for example, the signature values may decay tozero if the user were absent from the geographic location for anextended period. Thus, upon returning to that particular location, theuser would effectively have to “retrain” the system by rebuilding thesignatures corresponding to that location. The adaptive decay techniqueavoids this problem.

An illustration of an implementation of the adaptive decay techniquefollows. As mentioned above, signature decay occurs for all signaturesin coordinates (t, g, s) (i.e., time t, geographic location g, anddataspace s), only when there is a new activity in (t, g, s). Inaddition, a minimum time must elapse before decaying takes place. Toaccount for long elapsed times, the system uses the concept of epochtime. Epoch time is the absolute local time since a certain distantpast. The concept of epoch time can be found in current-day operatingsystems (e.g., Linux and WinCE) that fix a reference point in thedistant past and return the elapsed time since that reference point. Forthe example below, T is the epoch time when some activity x happens in(t, g, s). Note that the coordinate t is an integer denoting thediscretized time denoting the time-of-day or time slot, whereas T is anepoch time. For use in the Equation 22 below, β(t, g, s) is the decaythreshold for signatures, r(t, g, s) is the last time, in epoch units,that signatures in (t, g, s) were decayed, and e(t, g, s) is a vectorcapturing a newly performed user action (i.e., current signature) with aduration/count metric (explained above) in position x and zeros in allother positions. This technique also uses the smoothing parameter α asdescribed above. Equation 22, shown below, is one implementations theadaptive decay technique.

$\begin{matrix}{{{ks}\left( {t,g,s} \right)} = \begin{Bmatrix}{{{\alpha\;{{e\left( {t,g,s} \right)}\lbrack x\rbrack}} + {\left( {1 - \alpha} \right){{ks}\left( {t,g,s} \right)}}};} \\{{{if}\mspace{14mu} T} > {{r\left( {t,g,s} \right)} + {\beta\left( {t,g,s} \right)}}} \\{{{{ks}\left( {t,g,s} \right)} + {\frac{\alpha}{\left( {1 - \alpha} \right)}{{e\left( {t,g,s} \right)}\lbrack x\rbrack}}};} \\{otherwise}\end{Bmatrix}} & \left( {{Equation}\mspace{14mu} 22} \right)\end{matrix}$

Under this implementation, the system decays the signature if the timeinterval since the last decay is greater than the decay interval; inthis case, the system performs a convex combination of the past activityand present activity. If the last decay has occurred more recently thanthe decay interval, then the historic signature is combined with thecurrent signature, with a multiplier α/(1−α) applied to the currentsignature. The technique of using this multiplier optimizes storage.Typically, when performing an exponential smoothing operation, the pastis the period of time up to time r(t, g, s), and the present is theperiod of time from time r(t, g, s) to time T. Under a typicalapplication, the new activity x would be stored in a temporary storage,ts(t, g, s), along with all additional subsequent activities, until thetime r(t, g, s)+β(t, g, s). At that time, the smoothing formula wouldcombine the past with the new activities according to Equation 23.ks(t, g, s)=αts(t, g, s)+(1−α)ks(t, g, s)   (Equation 23)

The system avoids the need for temporary storage by combining each newactivity with the past signature as each new activity occurs, using themultiplier described above to offset what would otherwise be a prematurecomposition. This ensures true exponential smoothing. Although the abovediscussion involved only the keyword signatures, ks, the same principlesand techniques apply to all other signatures described herein.

Use of Signatures to Personalize Content

As mentioned above, one illustrative use of the learning system is toenhance the user experience during a search procedure. In oneillustrative implementation, the various individual, aggregate, andprogram signatures reside on a server system that contains a set ofcontent items (e.g., television programs, television channels, movies,etc.). The server system uses the signatures to personalize searchresults provided to users of the system. In particular, the resultsobtained through a query are identified and reordered by promotingrelevance values of individual search results based on the set ofsignatures. For example, in a system employing an incremental searchmethod (as described in the above incorporated U.S. PatentApplications), the system begins searching for content item results asthe user enters letters of a text query. The system identifies contentitems as candidates for presentation based on comparing the letters ofthe text query with descriptive terms associated with the content items.Each of these content items is associated with a base relevance valuethat measures the popularity of the item in the overall population. Thesystem uses these base relevance values to rank which content items arelikely sought by the user. Higher base relevance values indicate ahigher overall popularity, thus, these items are assumed to be of moreinterest to the user than items with lower base relevance values.

However, as explained in greater detail below, the system modifies thebase relevance values based on the set of user signatures. Thus, if theset of signatures indicates, given the particular time and day of thesearch, that it is likely the user is searching for a program with thegenre of news, the system will promote the relevance values of programswith a genre of news that match the user's query text. Likewise, thesystem can use the channel and microgenre data associated with thecontent items in conjunction with the channel and microgenre signaturesto promote the base relevance values. The final relevance weights ofeach item determine if the item is included in the result set and helpdetermine its position in a list of results. Many different promotiontechniques can be implemented; one example is the “ruthless promotiontechnique”, described below.

The ruthless promotion technique ensures that any particular searchresult item that has a nonzero probability in a user signature will haveits relevance value boosted such that it will be higher than any othersearch result items having a zero probability value in the same usersignature. For use in Equation 24 below, K is the number of searchresults retrieved with relevance numbers r₁, r₂, . . . , r_(K), and M isthe maximum value any relevance can have, based on the generalpopularity of the search result. Typically, search engines assign arelevance number to query results based on ranks with some maximumbound. These ranks can be a measure of the popularity or relevance ofthe items based on popular opinion. Search results are displayed in theshelf space, sorted in descending order of relevance based on theseranks. (Herein, the phrase “shelf space” refers to the portion of adisplay screen of a device that displays the search results in responseto a user query. This portion can be organized as a column of text boxesin some implementations.) The values p₁ ⁽¹⁾, p₂ ⁽¹⁾, . . . p_(K) ⁽¹⁾ arethe channel signature probabilities (0≦p_(i) ⁽¹⁾≦1) assigned by thelearning system (typically, most of the p_(i) ⁽¹⁾ will be 0). Thesuperscripts on the probabilities refer to type of signature, e.g.,channel, genre, or microgenre. The ruthless promotion technique computesnew relevance numbers {tilde over (r)}₁, {tilde over (r)}₂, . . . ,{tilde over (r)}_(K) as

$\begin{matrix}{{\overset{\sim}{r}}_{i} = \begin{Bmatrix}{\left\lfloor {\left( {M + 1} \right){\mathbb{e}}^{p_{i}^{(1)}}} \right\rfloor;} & {p_{i}^{(1)} > 0} \\{r_{i};} & {p_{i}^{(1)} = 0}\end{Bmatrix}} & \left( {{Equation}\mspace{14mu} 24} \right)\end{matrix}$

The search items are then reordered using the new relevance numbers. Forexample, a user had watched the channels “CARTOON NETWORK” and “COMEDYCHANNEL” in the past, with the signature probabilities 0.7 and 0.3respectively. The generic relevance numbers for channels, based onpopular opinion, are 500, 300, 100, and 70, for “CBS”, “CNBC”, “COMEDYCHANNEL”, and “CARTOON NETWORK”, respectively with a maximum bound of1000. Table 1 and Table 2 show the displayed results and theircorresponding relevance values, when a query character “C” is typed.Table 1 shows the order of the query results without the benefit of thelearning system, and Table 2 shows the order of the results using theruthless promotion technique. As can be seen, the user convenience isenhanced, because fewer scrolls are required to

TABLE 1 Channel Relevance Number CBS 500 CNBC 300 COMEDY 100 CHANNELCARTOON  70 NETWORK . . .

TABLE 2 Channel Relevance Number CARTOON 2015 NETWORK COMEDY 1351CHANNEL CBS  500 CNBC  300 . . .

Other promotion techniques are within the scope of the invention,including, for example, techniques that do not necessarily ensure that asearch result item that has a nonzero probability in a user signaturewill have its relevance value boosted such that it will be higher thanany other search result items having a zero probability value in thesame user signature. In particular, because there are six signaturescapturing the user activity at any time of day—channel, genre, andmicrogenre for given time slot, and their corresponding compositesignatures, these signatures are combined to compute new relevanceweights. Equation 24 above shows the use of channel signature forpromotion. In the example below, there is an inherent importance inthese signatures, from more refined to more coarse. This variant of theruthless promotion technique considers an aggregated promotion formula,as follows:

$\begin{matrix}{{\overset{\sim}{r}}_{i} = \left\lfloor {\sum\limits_{1 \leq k \leq 6}{\left( {M + 1} \right)^{k}{\mathbb{e}}^{p_{i}^{(k)}}}} \right\rfloor} & \left( {{Equation}\mspace{14mu} 25} \right)\end{matrix}$

In Equation 25, the superscript on the probabilities, P_(i) ^((k))refers to time slot channel, microgenre, genre, followed by compositechannel, microgenre, and genre signatures, with increasing values of k,respectively. Since they are being multiplied by powers of (M+1), anatural relevance importance is implied.

Signatures corresponding to overlapping periodicities are also combinedto provide an aggregate signature for a particular time slot. Theprobabilities in the aggregate signature can be used with the promotiontechniques above to identify and rank the results of search. In order toform an aggregate signature, the vectors from the overlapping signaturesare added together and renormalized. For example, for a particular timeslot, a user has a first signature with a periodicity of every Mondayand a second signature with a periodicity of every weekday. The firstsignature is the genre vector {(0.2 news), (0.8 sports)}; the secondsignature is the genre vector {(0.1 comedy), (0.4 news), (0.5 sports)}.To form an aggregate signature, the system first arithmetically combinesthe two vectors to produce the new vector {(0.1 comedy), (0.6 news),(1.3 sports)}, and the system then normalizes the new vector by dividingeach numerical element by the sum of the numerical elements of thevector, i.e., 2.0. Thus, the aggregate, normalized genre probabilityvector of the two overlapping signatures is {(0.05 comedy), (0.3 news),(0.65 sports)}.

Seminormalization of Signatures

In one implementation, the learning system uses integer arithmetic forrelevance promotion computations. In particular, all probabilities arerepresented as integers, appropriately scaled. One compelling motivationfor using integer arithmetic is to make the learning system portable todisparate hardware and operating systems, some of which may lackfloating arithmetic.

The learning system uses a seminormalization approach to weight morerecent activities more heavily in the signatures, while deemphasizing,but still retaining, the information from more distant activities in thesignature. Thus, when personalizing services or content provided to theuser, the system is more heavily influenced by more recent activities.The basic idea of this seminormalization approach is to make thelong-term memory coarse-grained by bucketing small probabilities thatresult from less common user preferences and/or preferences captured inthe more distance past, while still bounding the range of values byusing a small relevance scale factor. This approach allows the system tocapture both small and large probabilities without requiring a largedynamic range to define the probability bounds. Thus, a small scalingfactor is used to distinguish between the relatively more probableactivities in the captured in the signatures, while the relatively lessprobable activities are not lost due to truncation errors.

An illustrative technique for converting an unnormalized signature, x,into a seminormalized signature is provided below. In signature x, allelements x_(i) are nonnegative integers, representing the activityintensity. Signature x is an N-dimensional vector, and x has at leastone positive element. The value d is the infinity norm of x and

${{x}_{\infty}\overset{\Delta}{=}{\sum\limits_{1 \leq i \leq N}x_{i}}};$thus, d is the normalizing sum. The vector p is the normalizedprobability vector corresponding to x; therefore,

${p_{i} = \frac{x_{i}}{d}};{1 \leq i \leq {N.}}$

In order to seminormalize the signature x, the system uses a fine-grainmemory threshold of probabilities, K, represented in log scale; i.e., Kis a positive integer denoting a probability threshold 10^(−K). Allprobabilities≧10^(−K) will be scaled in fine-grain, and allprobabilities between 0 and 10^(−K) will be scaled in coarse-grain withbucketing. The system also uses a positive integer, S, as theseminormalization range represented in log scale. Afterseminormalization, a probability value of 1 is scaled to 10^(S). Thelargest value in the seminormalized vector would be 10^(S). Although notrequired, S can be equal to K. For use the in equations below, lett=10^(−K), 1=K+2, and u=10^(S). Finally, y is the seminormalized vectorcorresponding to p. Thus, y=f(p, K, S), where f is the functionimplementing the seminormalization algorithm. The function f is not aninvertible function.

Each element i of the seminormalized vector y is defined by the Equation26.

                                     (Equation  26)$y_{i} = \begin{Bmatrix}{1;} & {0 \leq p_{i} < 10^{{- 2}K}} \\{{v + 2};} & {10^{({{{- 2}K} + v})} \leq p_{i} < 10^{({{{- 2}K} + v + 1})}} \\\; & {0 \leq v \leq {K - 1}} \\{{\frac{\left( {{10^{K}1} - u} \right)}{\left( {10^{K} - 1} \right)} + \frac{10^{K}\left( {u - 1} \right)p_{i}}{\left( {10^{K} - 1} \right)}};} & {10^{- K} \leq p_{i} \leq 1}\end{Bmatrix}$

The first 2 parts of Equation 26 define the coarse-grain bucketing, andthe last part of the equation defines the fine-grain scaling. FIG. 7shows a pictorial representation of Equation 26. The X-axis is shown inlog scale. In FIG. 7, S=K, and there are K buckets of width 0.1. Thebuckets start with the bucket having a left boundary 10^(−2K) and endingwith the bucket with the right boundary 10^(−K). There is a specialunderflow bucket for any probability<10^(−2K). Each pi falling within abucket is scaled to a fixed count. For probabilities larger than10^(−K), the p_(i) is scaled using a linear equation. The slope of thelinear scaling equation in plot is approximately 10^(S) with theintercept at (K+2).

An example of this approach as applied to an electronic phonebookapplication on a mobile phone is provided below. In this example, eachoutgoing call is counted and stored in a raw signature. The systemscales the call counts by a large number so that truncation errors inthe smoothing and aging process, due to integer arithmetic, are reduced.FIG. 8 illustrates a raw phonebook signature 800 with 6 entries. The rownames in signature 800 indicate the person called, and the values arethe scaled frequency, after aging and smoothing. Thus, the value 1represents the long-term memory of a phone call made to John, perhapsmany years ago, and not repeated again. Similarly, the entrycorresponding to Jane has a signature value of 5. This value can beinterpreted two ways: (1) Jane was called as long ago as John, but witha frequency five times greater than John; or (2) Jane was called morerecently with the same frequency as John. The larger values representshort-term memories of calls made in the recent past. The normalizedprobabilities of these events are shown in a probability vector 810.

It is clear the dynamic range of probability vector 810 is quite large.Using the techniques described above, with K=S=4, the system generated aseminormalized vector 820. The system has collapsed the memory of Johnand Jane into the underflow bucket, thus making them indistinguishable.Some differentiation has been made for Debbie and Naren, although theseentries also represent long-term memories, and therefore, the exactdifference in frequency or recency of calls is not retained. However,the system captures the precise relative values of Simon and Marty. Thevalues in the seminormalized vector are completely bounded and suitablefor stable relevance promotion.

Activity Spike Detection

The learning system is also useful for detecting sudden bursts ofactivity (i.e., spike detection) at a particular time and day for aparticular search item, e.g., a certain television channel. The systemcan use these spikes of activity to temporarily boost the base relevancevalues of particular items that have sudden popularity. Typically,spikes happen when an emergency or crisis has happened in the recentpast. For example, if a particular news channel is presenting alate-breaking news story that is attracting a high number of viewers,the system will recognize the sudden popularity of the news program andboost its base relevance in recognition of the fact that other users ofthe system may also be interested in the news story.

In general, when the collective activity level associated with aparticular content item is above a certain threshold attributable tostatistical variations, then the activity level is considered a spike.The learning system analyzes the current and past activity levels bycollectively examining all of the signatures of the user population. Ifeach user is considered an independent random variable whose probabilityof watching a program is encoded in a stochastic signature, then thecollection of all these independent random variables provides a measureof the overall popularity of the content item. Thus, the system employsthese signatures to derive a joint probability distribution of thenumber of users watching a given program at a given time. Thus a newtype of signature, herein a “program signature”, r_(k) ^((i,t)), isdefined in Equation 27.r _(k) ^((i,t)):=Probability that a program i is being watched by kusers at time t; 0≦k≦N   (Equation 27)

An example of a technique for obtaining the program signature isprovided below. In general, when the activity level associated with aparticular content item exceeds a certain inherent randomness valuepredicted by the program signature, the system identifies such activityas a spike.

The system creates a set of program signatures, each of which is astatistical convolution of all individual signatures in the populationthat have watched the particular program. By convolving the individualsignatures, the system creates an aggregate mean and standard deviationof the activity level associated with the given program. Thus, a programsignature captures the fraction of all of the current users interactingwith the system that are currently watching the given program. Becausethe number of users interacting with the system changes over time, thefraction of users watching a particular program changes over time aswell. The system captures this information by creating programsignatures for the various time slots.

These signatures estimate the mean and higher moments of the probabilitydistribution of the number of people accessing this program in terms offractional probabilities. The aggregate signature and relatedstatistical measures define the normal level of activity for theparticular search item. Thus, by continually monitoring the currentlevel of activity for the particular search item at a given time, thesystem can detect if the current activity is above or below the normallevel. If the activity exceeds a certain threshold, the system adjuststhe reordering technique to temporarily boost the relevance of theparticular search item to recognize that the item may be of particularinterest.

An example of creating an aggregate signature is provided below. For thesake of simplicity, the example is restricted to one day, one time slot,one content item, i, and a single category of signature (e.g., channel,genre, or microgenre). This technique for finding the aggregatesignature is applied to all time periods, all days, all search items,and all signatures. In the following example, N is the number of usersusing the system, q_(i) ^((j)) is the normalized signature value of userj for item i (i.e., the fraction of time user j watched program i) where1≦j≦N, ψ is the index set {1, 2, . . . , N}, Φ_(m) is the set of subsetsof ψ of size m where 0≦m≦N, and X is a random variable denoting thenumber of users currently watching program i.

The unnormalized probability that there are m users watching program i,herein r_(m), is determined by Equation 28.

$\begin{matrix}{{r_{m} = {\sum\limits_{\Theta \in \Phi_{m}}{\prod\limits_{1 \leq k \leq m}q_{i}^{(j_{k})}}}},{{{where}\mspace{14mu}\Theta} = \left\{ {j_{1},j_{2},\ldots\mspace{11mu},j_{m}} \right\}}} & \left( {{Equation}\mspace{14mu} 28} \right)\end{matrix}$

The normalization constant, G, is given by Equation 29.

$\begin{matrix}{G = {\sum\limits_{0 \leq m \leq N}r_{m}}} & \left( {{Equation}\mspace{14mu} 29} \right)\end{matrix}$

The probability density function of X, f_(X)(m), the mean of X, μ_(X),and the standard deviation of X, σ_(X) are now given by the followingequations:

$\begin{matrix}{{{f_{X}(m)} = {\frac{1}{G}r_{m}}};{0 \leq m \leq N}} & \left( {{Equation}\mspace{14mu} 30} \right) \\{\mu_{X} = {\sum\limits_{0 \leq m \leq N}{{mf}_{X}(m)}}} & \left( {{Equation}\mspace{14mu} 31} \right) \\{\sigma_{X} = \sqrt{\sum\limits_{0 \leq m \leq N}{\left( {m - \mu_{X}} \right)^{2}{f_{X}(m)}}}} & \left( {{Equation}\mspace{14mu} 32} \right)\end{matrix}$

The system monitors the number of users watching program i. Chebychev'sinequality dictates that, with 96% confidence, the random variable Xcannot be above μ+5σ due to inherent randomness. Thus, whenever thenumber of users watching program i goes beyond μ_(X)+5 σ_(X), the systemidentifies this as a spike of activity. The system can temporarily boostthe base relevance of program i in queries for which program i is acandidate in recognition of the fact that the user may be interested inthe same program. The relevance can be boosted by a predeterminedamount, or it may be boosted by an amount that is proportional to theincrease in viewing of the program. In addition, the system can use avariety of multiples of σ_(X) (not only 5σ_(X)) to determine when aspike of activity is occurring.

The system can also infer the overall relevance of particular searchitems using the aggregate signatures. As described above, the systemcomputes the mean of the statistical convolution of N signatures, Nbeing the number of system users. Using this mean value, the systemgenerically reorders the search results even in the absence of asignature for a particular user. Thus, the user benefits from thesystems knowledge of the popular option of various search items, andthese popular opinions are used to identify and order search results forpresentation to the user. For example, if the aggregate signature has alarge mean for the television program “The Apprentice”, then any userwho does not have a personal signature will have this item in the topshelf on an appropriate query (the query, for instance, can be “trump”,which is a microgenre of the program “The Apprentice”).

FIG. 9 illustrates an example of detecting an increased level ofactivity associated with a content item (i.e., an activity spike). Anormal level of activity 905, as determined using the techniquesdescribed above is shown. Normal level of activity 905 is based on theaggregate signatures. As the system is being used, a current level ofactivity 910 is generated using continuously calculated aggregatesignatures based on the current content items usage or activity. Upondetecting an increase in activity level 915, which is beyond a specifiedthreshold, the system identifies the content item as having a spike ofactivity, and the system promotes the ranking of that content item, asdescribed above.

The learning system also allows accessing rare search items usingpreprocessing. In some implementations described above, the searchengines work by first gathering significant amounts of results matchingthe query, and filtering out low relevance results before applying apromotion technique. This technique has several advantages, includingincreasing the speed of the system and reduces network bandwidthrequired. However, a specific user may be interested in an item havinglow overall popularity that is filtered out of the results beforeapplying a promotion technique. In the absence of a signature, this rareitem may never me presented in the search results (this rare item issometimes referred to as the “long tail” in the probability distributionsense).

In order to capture the rare item in the ordered search results, someimplementations of the system compute the relevance before filtering,using the promotion techniques described above or other promotiontechniques. Thus, the rare item is ranked highly for the particularuser, allowing him or her to access the item with ease. Here, signaturesenable fine-grain customization and increase user satisfaction.

An inherent feature of the stochastic signature mechanism is theprobabilistic nature of the signature entries, i.e., the signatureentries are all normalized probabilities. This enables the system toexport the signatures to other, potentially unrelated systems, withease. For example, over some period of time, the television systeminterface described above learns that, in general, a given user prefersthe Spirituality genre 50% of the time, Sports genre 40% of the time,and the Seinfeld show 10% of the time. In response, the system creates aset of signatures for the user that captures these preferences. The usercan elect to share this signature information with other systems.

Therefore, when the user registers with a website that sells books, theuser can elect to share his signature information with this website.Because the signature information is stored in terms of normalizedprobabilities, the signature can be easily imported into the websitethat is configured to utilize such probability information. In addition,the website need not have an identical set of genres as that of thetelevision system in order to use the signature information. Forexample, the website may not have “Seinfeld” defined as a genre orcategory of books. In this case, the website can simply renormalize thesignature by removing the irrelevant entries, i.e., Seinfeld, anddetermining new normalized probabilities for the remaining genres. Thus,the new normalized probabilities for the user would be 56% forSpirituality and 44% for Sports. Sharing signatures in this way obviatesthe need for relearning in the new system. Also, different subsets ofsignatures can be shared for different systems.

Signature Based Preference Service

As explained above, the learning system captures the user's preferencesacross multiple dataspaces. In addition, portions of the learning systemcan be incorporated into various user client devices, thereby enablingthe system to capture the user's preferences across multiple devices.For example, the system can track the user's actions performed on amobile telephone, a television system, a handheld computer device,and/or a personal computer. This enables the system to providepersonalized services to the user across the multiple dataspaces andmultiple devices. Thus, user preferences expressed on one device can beused to personalize the user interactions on a different device.

Likewise, the learning system can provide the learned user preferencescaptured in the various signatures to third-party service providers. Theinformation provided to third-party service providers allows the serviceproviders to personalize the services for the user outside of thelearning system. In such an implementation, the learning systemdetermines what preference information to provide to the serviceproviders based on the nature of the services provided. The learningsystem can provide this information on a per transaction basis, or thesystem can periodically synchronize a set of signatures stored by thethird-party service provider. Furthermore, the user can configure thelearning system so as to control which third-party service receives userpreference information.

By providing a centralized system that learns and stores the user'spreferences, the learning system enables the user to avoid creatingdisconnected pockets of personalization associated with only oneparticular dataspace or device. Therefore, a user may immediatelyleverage the preference information contained in the user's signatureswhen interacting with a new service rather than having to wait for thenew service to learn the user preferences. Thus, the learning system canprovide personalization information to the third-party service providerto improve the user's experience with the service.

This comprehensive personalization across diverse user activities anddevices is especially helpful to the user when the user interacts withthe same service provider using different interface devices. Not onlydoes the learning system capture the user's preferences from thesediverse interactions, but the system also stores the details of theuser's transaction for later retrieval by the user. For example, a usercan book a flight through a travel website using a personal computer.The learning system captures the detailed information associated withtransaction, e.g., the date of travel, the time of the flight, and thedeparture and destination city. At a later time, the user wishes tomodify the travel reservations, and elects to do so using a mobiletelephone. Because the system monitors the user's interactions withvarious service providers, the system recognizes that the user hasplaced a telephone call to the travel service. In response, the learningsystem automatically presents the user's upcoming travel itineraries onthe user's mobile telephone or sends the information to the travelservice's customer service agent with the user's consent.

In the alternative, if the user is presented with an automated voiceresponse system, the learning system can send the relevant itinerariesto the travel service (e.g., via an SMS message dispatched to thetelephone number called or DTMF tones at the beginning of the telephonecall), which would provide the travel service with a background contextof the telephone call to improve the automated voice response system'sresponse to the user voice commands. The power of a comprehensivepersonalization across diverse user activities and devices becomes veryevident in voice based navigation applications. Comprehensivepersonalization can provide the necessary context that can vastlyimprove ambiguities in user input that plague these types of systemstoday.

FIG. 10 illustrates a part of the learning system for providing acontext specific personal preference information service. In a preferredembodiment, a user device 1001 a-c solicits a service, on behalf of theuser, from a service provider 1002. This can include, for example,making a telephone call to modify a travel itinerary or accessing asearch engine to find some information. The context-sensitive personalpreference information service 1003 enables the external serviceprovider 1002 to provide a targeted response to the user based on user'sprior activity, data access history, and the learned user preferences.

Service provider 1002 can also serve as the source of information andrelevance updates for user device 1001 a-c. A network 1002 functions asthe distribution framework and can be a combination of wired andwireless connections. The navigation devices can have a wide range ofinterface capabilities and include such devices as a personal or laptopcomputer 1001 a, a hand-held device 1001 b (e.g. phone, PDA, or amusic/video playback device) with limited display size and an overloadedor small QWERTY keypad, and a television remote control system 1001 c,wherein the remote control has an overloaded or small QWERTY keypad. Thenavigation devices provide user activity data to the learning system viapersonal preference information service 1003 to create the varioussignatures. As mentioned above, in alternate embodiments, the userdevice can create the various signatures, and the signatures can be kepton the device. This enables the device to locally filter and ordercontent items received from service provider 1002 and/or content itemsthat reside on the device itself.

As described above, the learning system captures the user's preferencesfrom the user's interactions with various dataspaces. FIG. 11illustrates the local tracking and strengthening of the personalpreference signatures based on user activity and the content on a mobiledevice. For example, user interaction with a telephone book 1101, mediaapplications 1102, email/calendar 1103, and web browser 1104 aretracked, as well as when and where the interaction takes place. Inaddition to the user's interaction with these applications, the contentthat is coupled with these applications such as call logs 1101A, musicfiles 1102A, email data/calendar appointments 1103A, and browser cookies1104A are also tracked to capture the user's preferences. Aggregatedactions and various signatures 1105 are captured by the learning systemas described above.

The aggregated data and signatures 1105 are used by a wide variety ofservices, ranging from a local data prefetching service, in order toimprove search performance, to a commercial third-party serviceprovider, in order target the user for a specific product offering. Thesets of signatures generated by the learning system form an onion-likelayered structure; the inner layers are specific and capture the exactdetails of the user's actions, while the outer layers characterize theuser's general preferences. For example, the inner layers capture (1)the time and the location where the user performed an action, (2) thenature of the action (e.g. tuning to a channel or the purchase of abook, DVD, or airline ticket), and (3) the details of the action (e.g.the channel and/or program the user tuned to, the title of book the userordered, or the departure and destination airports of an airline ticketpurchase). This layered structure coincides with the various signaturescreated by the learning system. The inner layers correspond to themicrogenre and keyword signatures, while the outer layers correspond tothe genre signatures.

The service provider requesting the user's signature information candesignate the degree of specificity of user preferences desired. Forexample, a video content search engine wishing to use the user'ssignatures to order the results of a query may request specificinformation on which channels or program the user watched. A bookstore,on the other hand, may request broad user preferences of book tastes.The personal signature information sent in the later case would not bethe individual instances of purchased books, but rather the broad userpreferences at a genre level.

FIG. 12 illustrates the information flow when a user device 1203 makes arequest to a service provider 1201 (step 1). The request contains aunique identifier that identifies the user or the user device. Theidentity could be an anonymous yet unique identifier of the device. Forexample, a one-way hash function of the device hardware identifier maybe used to uniquely identify the device; there would be no way toreverse map to the actual device that generated the response, given theone-way nature of the hash function. In this case, the personalpreference service 1202 has only have a set of unique device identifiersthat share signatures for each user; there would be no identityinformation beyond the identifiers. In this way, the user's identity ismaintained anonymous, yet responses matching user's preferences can bedelivered.

In addition to the substance of the request, the communication from userdevice 1203 to service provider 1201 contains information that describesthe context of the request, as explained below. Service provider 1201communicates the substance of the request and the additional contextinformation to personal preference service 1202 (step 2). The contextinformation includes the identifier of the user device currently beingemployed, the location of the user device, if available, the time of therequest, and general description of the action the user is performing(e.g., the fact the user is currently using a telephone versus playingmedia). The additional context information enables personal preferenceservice 1202 to provide context-sensitive personal preferenceinformation to service provider 1201. Descriptive tags are assigned tothe various actions the user can perform using the system. The systemassociates these descriptive tags with the signatures that are generatedby the corresponding actions. In this way, personal preference service1202 sends relevant preference information based on the tags sent byuser device 1203 to service provider 1201 (step 3).

The relevant personal preference information is used by the serviceprovider 1201 to send a targeted response to the user device 1203 (step4). Additionally, service provider 1201 sends feedback to personalpreference service 1202 about the targeted response that was sent (step5). This feedback is used by personal preference service 1202 to adjustthe personal actions signature of the user.

By disaggregating personal preferences through a standalone entity, i.e.personal preference service 1202, multiple service providers thatprovide different services can all benefit from the aggregated personalpreference accrued across different service providers, different useractions, and different user devices. The end user gains immensely due tothe key benefit of having targeted responses to many different types ofrequests. For example, a user who purchases books from Amazon.com getsthe benefit of a targeted response when he goes to the Barnes & Noblessite using the techniques described above.

As described above, personal preference service 1202 can also be acentralized aggregator of user actions across different devices. Thus,user actions performed on different devices, e.g., a mobile computingdevice, a home television with set-top box, and a personal computer,could all be aggregated to provide user preferences for identifying andordering search results. For example, a user could initiate a remoterecording for a favorite program using a mobile device, where thediscovery of the desired program can be made easy by leveraging theuser's viewing behavior on a television system. Thus, the availableepisodes of Seinfeld could be automatically displayed in the mobiledevice, for example, based on the fact that the user has viewed Seinfeldmany times in the past on the television system.

FIG. 13 illustrates the information flow when a user device 1302 makes arequest to a service provider 1301. In this scenario, the contextsensitive personal preference information is sent along with the request(step 1) to generate a response (step 2). The personal preference data1303 is locally resident on user device 1302. Additionally, personalpreference data 1303 is updated (step 3) based on the response receivedfrom service provider 1301.

In another implementation of the learning system, a user device canserve as the personal preference provider in a peer-to-peer fashion forother user devices. For example, in a home entertainment network withmore than one DVR (Digital Video Recorder), one DVR can serve as thepersonal preference provider for another DVR resident in the same home.When the user performs a search for content on a particular DVR, theother DVR in the home provides a personalized preference service toenable the user to find the desired content more quickly by leveragingthe prior user viewing habits across the different DVRs.

In addition, a particular user can elect to share his or her signatureswith another user. This can be accomplished in a peer-to-peer fashion asdescribed above. In this case, the preferences learned for one user areused to personalize content results for another user. For example, thesystem will generate a set of signatures for a first user while thatuser selected various content from a book dataspace. These signaturesencode the book reading preferences of the first user. A second user hasa similar interest to the first user, and the second user wishes toselect books related to similar topics as the first user. In this case,the first user can share his signature with the second user. The systemthen uses the first user's signatures to personalize the content resultsfor the second user. In this way, the system enables the second user tobenefit from the learned preferences of the first user without thesecond user having to train the system.

FIG. 14 illustrates different services 1401, for example, travelservices (airline, car, and hotel), food services, entertainmentservices, and search engines services, that benefit from thecontext-sensitive personal preference service 1402. Although eachservice provider may have its own personalized services, when usersfirst identify themselves, the services have no knowledge of the firsttime customer. The techniques disclosed herein increase the likelihoodof the acquiring and retaining first time customers by offering targetedservices immediately upon using the service. The techniques disclosedalso enhance the first-time user experience. In contrast, without thesetechniques, users would have to create an account with a service andbuild an action history with that service before receiving personalizedservices.

Using the techniques described above, a user, for example, can go to anytravel site and the site, without knowing the user and without requiringhim to create an account or log in, can still offer the userpersonalized services based on the history of prior travel actions theuser took on other platforms or web sites. Additionally, for serviceswhere comprehensive personalization is not in place, these services canleverage the personal preference service discussed above.

Because the learning system and personal preference service operateacross multiple dataspaces and multiple user devices, the user deviceconfiguration can vary greatly. FIG. 15 illustrates possible user deviceconfigurations for use with the learning system and thecontext-sensitive personal preference service. In one configuration, auser device 1509 can have multiple output capabilities, for example, adisplay 1501 and/or a voice output 1502. In addition, user device canhave a processor 1503, volatile memory 1504, a text input interface1505, and/or voice input 1506. Furthermore, user device 1509 can haveremote connectivity 1507 to a server through a network and can havepersistent storage 1508.

In another user device configuration, user device 1509 may not havelocal persistent storage 1508. In such a scenario, user device 1509would have remote connectivity 1507 to submit the user's request to aserver and retrieve responses from the server. In yet anotherconfiguration of user device 1509, the device may not have remoteconnectivity 1507. In such case, the learning system, personalizationdatabase, and signatures are locally resident on local persistentstorage 1508. Persistent storage 1508 can be a removable storageelement, such as SD, SmartMedia, or a CompactFlash card. In aconfiguration of user device 1509 with remote connectivity 1507 andpersistent storage 1508, user device 1509 can use remote connectivity1507 for a personalization data update or for the case where thepersonalization database is distributed between local persistent storage1508 and a server.

It will be appreciated that the scope of the present invention is notlimited to the above-described embodiments, but rather is defined by theappended claims, and these claims will encompass modifications of andimprovements to what has been described. For example, embodiments havebeen described in terms of a television content system. However,embodiments of the invention can be implemented on a mobile phone toassist the user in retrieving personal contact information forindividuals.

1. A user-interface method of selecting and presenting a collection ofcontent items in which the presentation is ordered at least in partbased on learned user preferences, the method comprising: providing aset of content items, each content item having at least one associateddescriptive term to describe the content item; receiving incrementalinput entered by the user for incrementally identifying desired contentitems; in response to the incremental input entered by the user,presenting a subset of content items; receiving selection actions ofcontent items of the subset from the user; analyzing the descriptiveterms associated with the selected content items to learn the preferreddescriptive terms of the user; expressing the learned preferreddescriptive terms as a segmented probability distribution functionhaving at least one fine grain segment and at least one coarse grainsegment, wherein the fine grain segment has fine grain differentiationof probability weights associated with preferred descriptive termswithin the segment, and wherein the coarse grain segment has relativelycoarse grain differentiation of probability weights associated withpreferred descriptive terms within the segment; and in response toreceiving subsequent incremental input entered by the user, selectingand ordering a collection of content items by promoting the ranking ofcontent items associated with the learned preferred descriptive terms ofthe user according to the differentiation provided by the segmentedprobability distribution function; wherein the segmented probabilitydistribution function further having an overflow segment, wherein theprobabilities weights within the overflow segment are not differentiatedfrom other probabilities weights within the overflow segment, andwherein probabilities weights within the overflow segment aredifferentiated from the probability weights within the coarse and finegrain segments; and wherein at least one of the incremental input andthe subsequent incremental input are entered by the user on an inputconstrained device.
 2. The method of claim 1, wherein the probabilityweights of the segmented probability distribution function associatedwith preferred descriptive terms are based on the frequency of selectionof content items associated with said preferred descriptive terms. 3.The method of claim 1, wherein the probability weights of the segmentedprobability distribution function associated with preferred descriptiveterms are based on the recency of selection of content items associatedwith said preferred descriptive terms.
 4. The method of claim 1, whereinthe probability weights of the segmented probability distributionfunction associated with preferred descriptive terms are based on thenumber of selections of content items associated with said preferreddescriptive terms.
 5. The method of claim 1, wherein the coarse grainsegment includes at least two weight groups, each weight group having apreselected range of probability weight values that determine whichprobability weights are in the weight group so that any probabilityweights in a particular weight group are not differentiated from otherprobability weights in the same group, and wherein the probabilitiesweights in different weight groups are differentiated from each other.6. The method of claim 5, wherein each weight group includes a highprobability weight value and a low probability weight value defining thepreselected range of probability weight values.
 7. The method of claim6, wherein the high probability weight value and the low probabilityweight value are separated by at least one order of magnitude.
 8. Themethod of claim 1, wherein the probability weights associated withpreferred descriptive terms are integer values.
 9. The method of claim1, wherein the learned preferred descriptive terms are further based on:analyzing the date, day, and time of the user selection actions andanalyzing the descriptive terms associated with the selected contentitems to learn a periodicity of user selections of similar contentitems, wherein similarity is determined by comparing the descriptiveterms of the selected content item with the previously selected contentitem, and wherein the periodicity indicates the amount of time betweenuser selections of similar content items relative to a reference point;and associating the learned periodicity with descriptive termsassociated with the similar content items; wherein the selecting andordering the collection of content items is further based on promotingthe ranking of those content items associated with descriptive termsfurther associated with periodicities similar to the date, day, and timeof the subsequent incremental input.
 10. The method of claim 1, whereinthe selecting and ordering the collection of content items is furtherbased on popularity values associated with the content items, eachpopularity value indicating a relative measure of a likelihood that thecorresponding content item is desired by the user.
 11. The method ofclaim 1, wherein the set of content items includes at least one oftelevision program items, movie items, audio/video media items, musicitems, contact information items, personal schedule items, web contentitems, and purchasable product items.
 12. The method of claim 1, whereinthe set of content items includes at least one of television programitems, movie items, and audio/video media items and the descriptiveterms includes at least one of title, cast, director, contentdescription, and keywords associated with the content.
 13. The method ofclaim 1, wherein the set of content items is contained on at least oneof a cable television system, a video-on-demand system, an IPTV system,and a personal video recorder.
 14. The method of claim 1, wherein theinput constrained device has a plurality of overloaded keys, each of theoverloaded keys representing two or more characters.
 15. The method ofclaim 1, wherein at least one of the incremental input and thesubsequent incremental input are entered by the user on at least one ofa telephone, a PDA, a computer, and a remote control.
 16. The method ofclaim 1, further comprising presenting the ordered collection of contentitems on at least part of a television screen.
 17. The method of claim1, further comprising presenting the ordered collection of content itemson a display constrained device.
 18. The method of claim 17, wherein thedisplay constrained device is at least one of a telephone, a PDA, and aremote control.
 19. The method of claim 1, wherein at least one of theincremental input and the subsequent incremental input comprises atleast one prefix of a word for describing the desired content items. 20.The method of claim 19, wherein at least one of the incremental inputand the subsequent incremental input comprises at least two prefixes ofa phrase for describing the desired content items.
 21. The method ofclaim 1, wherein at least one of receiving incremental input, presentingthe subset of content items, receiving selection actions, analyzing thedescriptive terms, expressing the learned preferred descriptive terms asa segmented probability distribution function, and selecting andordering the collection of content items is performed on a server systemremote from the user.
 22. The method of claim 1, wherein at least one ofreceiving incremental input, presenting the subset of content items,receiving selection actions, analyzing the descriptive terms, expressingthe learned preferred descriptive terms as a segmented probabilitydistribution function, and selecting and ordering the collection ofcontent items is performed on a user client device.
 23. The method ofclaim 1, wherein the segmented probability distribution is stored on auser client device and selecting and ordering the collection of contentitems includes selecting and ordering content items stored on the clientdevice.
 24. The method of claim 1, further comprising: organizing thecontent items of the set of content items into groupings based on theinformational content of the content items; determining a context inwhich the user performed the selection actions, the context including atleast one of geographic location of the user, day, date, time, and thegroup into which the selected content items are organized; andassociating the contexts of the user selection actions with thepreferred descriptive terms learned from the corresponding userselections; wherein only preferred descriptive terms associated with thecontext in which the user entered the subsequent incremental input areused in the selecting and ordering of the collection of content items.